Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f4973660748>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f49735c1780>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.1.0

/home/tia/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs = tf.placeholder(tf.float32, shape=(None,image_width,image_height,image_channels), name='inputs')
    z = tf.placeholder(tf.float32, shape=(None,z_dim), name='z')
    lr = tf.placeholder(tf.float32, name='learning_rate')
    return inputs, z, lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    # Hyperparameters
    alpha = 0.2  
    h1_filters = 128
    h2_filters = 256
    h3_filters = 512
    
    #build model
    with tf.variable_scope('discriminator', reuse=reuse):
        #images_channel = images.get_shape().as_list()[3]
        h1 = tf.layers.conv2d(images, h1_filters, 5, strides=2, padding='same')
        h1 = tf.maximum(alpha * h1, h1)
        #now 14 x 14 x 128
        
        h2 = tf.layers.conv2d(h1, h2_filters, 5, strides=2, padding='same')
        h2 = tf.layers.batch_normalization(h2, training=True)
        h2 = tf.maximum(alpha * h2, h2)
        #now 7 x 7 x 256
        
        h3 = tf.layers.conv2d(h2, h3_filters, 3, strides=2, padding='same')
        h3 = tf.layers.batch_normalization(h3, training=True)
        h3 = tf.maximum(alpha * h3, h3)
        #now 4 x 4 x 512
        
        
        flat = tf.reshape(h3, (-1, 4 * 4 * h3_filters))
        
        logits = tf.layers.dense(flat,1)
        out = tf.nn.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    # Hyperparameters
    alpha = 0.2  
    h1_filters = 512
    h2_filters = 256
    h3_filters = 128
    
    #build model
    with tf.variable_scope('generator', reuse=not is_train):
        h1 = tf.layers.dense(z, 3 * 3 * h1_filters)
        h1 = tf.reshape(h1, (-1, 3, 3, h1_filters))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = tf.maximum(alpha * h1, h1)
        #now 3 x 3 x 512
        
        h2 = tf.layers.conv2d_transpose(h1, h2_filters, 3, strides=2, padding='valid')
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.maximum(alpha * h2, h2)
        #now 7 x 7 x 256
        
        h3 = tf.layers.conv2d_transpose(h2, h3_filters, 5, strides=2, padding='same')
        h3 = tf.layers.batch_normalization(h3, training=is_train)
        h3 = tf.maximum(alpha * h3, h3)
        #now 14 x 14 x 128
        
        logits = tf.layers.conv2d_transpose(h3, out_channel_dim, 5, strides=2, padding='same')
        image = tf.nn.tanh(logits)
        return image


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    # Hyperparameters
    smooth = 0.1
    
    out_real, logits_real = discriminator(input_real)
    
    image_fake = generator(input_z, out_channel_dim, is_train=True)
    
    out_fake, logits_fake = discriminator(image_fake, reuse=True)
    
    d_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(logits_real) * (1 - smooth), 
                                                     logits=logits_real))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(logits_fake),
                                                          logits=logits_fake))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(logits_fake),
                                                          logits=logits_fake))
    
    return d_loss + d_loss_fake, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    #saver = tf.train.Saver()

    steps = 0
        
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr, beta1)
        
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # real images rescale
                batch_images = batch_images * 2.0
                
                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
                
                if steps % 10 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

Epoch 1/2... Discriminator Loss: 0.5156... Generator Loss: 5.7589
Epoch 1/2... Discriminator Loss: 1.5636... Generator Loss: 0.5048
Epoch 1/2... Discriminator Loss: 0.6073... Generator Loss: 2.4074
Epoch 1/2... Discriminator Loss: 1.1610... Generator Loss: 6.2258
Epoch 1/2... Discriminator Loss: 0.6811... Generator Loss: 4.8196
Epoch 1/2... Discriminator Loss: 0.5586... Generator Loss: 2.3557
Epoch 1/2... Discriminator Loss: 0.5487... Generator Loss: 2.3241
Epoch 1/2... Discriminator Loss: 0.4802... Generator Loss: 3.5235
Epoch 1/2... Discriminator Loss: 0.9406... Generator Loss: 3.4780
Epoch 1/2... Discriminator Loss: 0.8560... Generator Loss: 1.1576

Epoch 1/2... Discriminator Loss: 0.6407... Generator Loss: 2.1974
Epoch 1/2... Discriminator Loss: 0.7361... Generator Loss: 1.8424
Epoch 1/2... Discriminator Loss: 0.6390... Generator Loss: 2.1199
Epoch 1/2... Discriminator Loss: 0.7263... Generator Loss: 1.4493
Epoch 1/2... Discriminator Loss: 0.8940... Generator Loss: 3.7485
Epoch 1/2... Discriminator Loss: 0.5859... Generator Loss: 2.8066
Epoch 1/2... Discriminator Loss: 0.6692... Generator Loss: 1.9261
Epoch 1/2... Discriminator Loss: 0.8702... Generator Loss: 1.2862
Epoch 1/2... Discriminator Loss: 0.7834... Generator Loss: 1.3113
Epoch 1/2... Discriminator Loss: 0.7467... Generator Loss: 1.4407

Epoch 1/2... Discriminator Loss: 1.0635... Generator Loss: 3.3336
Epoch 1/2... Discriminator Loss: 0.8711... Generator Loss: 1.1537
Epoch 1/2... Discriminator Loss: 0.7327... Generator Loss: 2.1697
Epoch 1/2... Discriminator Loss: 0.7142... Generator Loss: 1.5911
Epoch 1/2... Discriminator Loss: 0.8927... Generator Loss: 3.3599
Epoch 1/2... Discriminator Loss: 1.1747... Generator Loss: 3.1110
Epoch 1/2... Discriminator Loss: 0.8894... Generator Loss: 1.9892
Epoch 1/2... Discriminator Loss: 0.7548... Generator Loss: 1.6711
Epoch 1/2... Discriminator Loss: 0.6928... Generator Loss: 1.9294
Epoch 1/2... Discriminator Loss: 0.7112... Generator Loss: 1.4647

Epoch 1/2... Discriminator Loss: 0.6965... Generator Loss: 2.3144
Epoch 1/2... Discriminator Loss: 0.6658... Generator Loss: 1.7898
Epoch 1/2... Discriminator Loss: 0.8612... Generator Loss: 2.3449
Epoch 1/2... Discriminator Loss: 1.1625... Generator Loss: 4.2994
Epoch 1/2... Discriminator Loss: 0.7560... Generator Loss: 1.2609
Epoch 1/2... Discriminator Loss: 0.6865... Generator Loss: 1.4957
Epoch 1/2... Discriminator Loss: 0.7407... Generator Loss: 1.4043
Epoch 1/2... Discriminator Loss: 0.6019... Generator Loss: 2.1169
Epoch 1/2... Discriminator Loss: 0.7154... Generator Loss: 2.8160
Epoch 1/2... Discriminator Loss: 0.6241... Generator Loss: 2.2607

Epoch 1/2... Discriminator Loss: 1.0892... Generator Loss: 3.3974
Epoch 1/2... Discriminator Loss: 0.7623... Generator Loss: 1.2457
Epoch 1/2... Discriminator Loss: 0.6112... Generator Loss: 2.4251
Epoch 1/2... Discriminator Loss: 0.6350... Generator Loss: 1.7797
Epoch 1/2... Discriminator Loss: 0.8125... Generator Loss: 1.2408
Epoch 1/2... Discriminator Loss: 1.0182... Generator Loss: 0.9300
Epoch 1/2... Discriminator Loss: 0.6593... Generator Loss: 2.0481
Epoch 1/2... Discriminator Loss: 0.7620... Generator Loss: 1.3055
Epoch 1/2... Discriminator Loss: 0.6311... Generator Loss: 1.8252
Epoch 1/2... Discriminator Loss: 1.0336... Generator Loss: 0.7815

Epoch 1/2... Discriminator Loss: 0.6768... Generator Loss: 1.7385
Epoch 1/2... Discriminator Loss: 0.6733... Generator Loss: 1.7628
Epoch 1/2... Discriminator Loss: 0.9756... Generator Loss: 2.4644
Epoch 1/2... Discriminator Loss: 0.9443... Generator Loss: 0.9789
Epoch 1/2... Discriminator Loss: 0.8015... Generator Loss: 3.0220
Epoch 1/2... Discriminator Loss: 0.9261... Generator Loss: 0.9614
Epoch 1/2... Discriminator Loss: 0.7460... Generator Loss: 1.4808
Epoch 1/2... Discriminator Loss: 0.7453... Generator Loss: 1.5319
Epoch 1/2... Discriminator Loss: 0.7355... Generator Loss: 2.6748
Epoch 1/2... Discriminator Loss: 0.8316... Generator Loss: 1.2487

Epoch 1/2... Discriminator Loss: 0.6956... Generator Loss: 1.9702
Epoch 1/2... Discriminator Loss: 0.7832... Generator Loss: 2.3523
Epoch 1/2... Discriminator Loss: 0.7441... Generator Loss: 1.3560
Epoch 1/2... Discriminator Loss: 0.6428... Generator Loss: 1.5919
Epoch 1/2... Discriminator Loss: 1.0706... Generator Loss: 0.8089
Epoch 1/2... Discriminator Loss: 0.8162... Generator Loss: 2.1384
Epoch 1/2... Discriminator Loss: 0.8908... Generator Loss: 1.0995
Epoch 1/2... Discriminator Loss: 0.7072... Generator Loss: 2.3068
Epoch 1/2... Discriminator Loss: 0.7654... Generator Loss: 1.9409
Epoch 1/2... Discriminator Loss: 0.8917... Generator Loss: 1.0187

Epoch 1/2... Discriminator Loss: 0.6480... Generator Loss: 1.8274
Epoch 1/2... Discriminator Loss: 0.8536... Generator Loss: 1.1019
Epoch 1/2... Discriminator Loss: 0.7726... Generator Loss: 2.4708
Epoch 1/2... Discriminator Loss: 0.8698... Generator Loss: 2.7495
Epoch 1/2... Discriminator Loss: 0.8185... Generator Loss: 1.2789
Epoch 1/2... Discriminator Loss: 0.8296... Generator Loss: 1.0955
Epoch 1/2... Discriminator Loss: 0.9038... Generator Loss: 1.0432
Epoch 1/2... Discriminator Loss: 0.6500... Generator Loss: 1.9469
Epoch 1/2... Discriminator Loss: 0.8676... Generator Loss: 1.0806
Epoch 1/2... Discriminator Loss: 0.9609... Generator Loss: 0.9461

Epoch 1/2... Discriminator Loss: 0.6572... Generator Loss: 1.7249
Epoch 1/2... Discriminator Loss: 0.7552... Generator Loss: 2.1371
Epoch 1/2... Discriminator Loss: 0.8201... Generator Loss: 2.6138
Epoch 1/2... Discriminator Loss: 0.9139... Generator Loss: 2.8020
Epoch 1/2... Discriminator Loss: 0.8092... Generator Loss: 1.1872
Epoch 1/2... Discriminator Loss: 0.6576... Generator Loss: 1.7450
Epoch 1/2... Discriminator Loss: 0.7393... Generator Loss: 1.3114
Epoch 1/2... Discriminator Loss: 1.0238... Generator Loss: 0.8798
Epoch 1/2... Discriminator Loss: 0.7953... Generator Loss: 1.3964
Epoch 1/2... Discriminator Loss: 0.7411... Generator Loss: 2.6819

Epoch 1/2... Discriminator Loss: 0.8101... Generator Loss: 1.1294
Epoch 1/2... Discriminator Loss: 0.7346... Generator Loss: 1.3427
Epoch 1/2... Discriminator Loss: 1.0301... Generator Loss: 0.9126
Epoch 2/2... Discriminator Loss: 0.7489... Generator Loss: 1.3355
Epoch 2/2... Discriminator Loss: 0.8279... Generator Loss: 2.6445
Epoch 2/2... Discriminator Loss: 0.6768... Generator Loss: 1.6210
Epoch 2/2... Discriminator Loss: 1.2060... Generator Loss: 0.7127
Epoch 2/2... Discriminator Loss: 0.8295... Generator Loss: 1.3322
Epoch 2/2... Discriminator Loss: 0.6640... Generator Loss: 1.6247
Epoch 2/2... Discriminator Loss: 0.9139... Generator Loss: 0.9402

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 4

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

Epoch 1/4... Discriminator Loss: 0.6634... Generator Loss: 1.6905
Epoch 1/4... Discriminator Loss: 0.4642... Generator Loss: 2.5747
Epoch 1/4... Discriminator Loss: 0.3919... Generator Loss: 3.6807
Epoch 1/4... Discriminator Loss: 0.4351... Generator Loss: 4.9298
Epoch 1/4... Discriminator Loss: 0.4261... Generator Loss: 2.8850
Epoch 1/4... Discriminator Loss: 0.7053... Generator Loss: 2.5144
Epoch 1/4... Discriminator Loss: 0.9889... Generator Loss: 1.3932
Epoch 1/4... Discriminator Loss: 0.6682... Generator Loss: 2.6458
Epoch 1/4... Discriminator Loss: 2.3679... Generator Loss: 10.7066
Epoch 1/4... Discriminator Loss: 1.5836... Generator Loss: 6.4616

Epoch 1/4... Discriminator Loss: 0.5527... Generator Loss: 2.3471
Epoch 1/4... Discriminator Loss: 0.5237... Generator Loss: 2.3599
Epoch 1/4... Discriminator Loss: 0.5476... Generator Loss: 4.1704
Epoch 1/4... Discriminator Loss: 0.8951... Generator Loss: 1.4519
Epoch 1/4... Discriminator Loss: 0.7037... Generator Loss: 3.6779
Epoch 1/4... Discriminator Loss: 0.6481... Generator Loss: 1.7538
Epoch 1/4... Discriminator Loss: 0.5138... Generator Loss: 2.9275
Epoch 1/4... Discriminator Loss: 3.3002... Generator Loss: 8.7491
Epoch 1/4... Discriminator Loss: 0.6838... Generator Loss: 2.2489
Epoch 1/4... Discriminator Loss: 0.7836... Generator Loss: 1.4665

Epoch 1/4... Discriminator Loss: 0.8618... Generator Loss: 1.8692
Epoch 1/4... Discriminator Loss: 1.0089... Generator Loss: 2.7127
Epoch 1/4... Discriminator Loss: 2.0106... Generator Loss: 6.6721
Epoch 1/4... Discriminator Loss: 1.1128... Generator Loss: 0.7101
Epoch 1/4... Discriminator Loss: 0.8023... Generator Loss: 1.6204
Epoch 1/4... Discriminator Loss: 1.2837... Generator Loss: 0.7271
Epoch 1/4... Discriminator Loss: 0.8180... Generator Loss: 1.3958
Epoch 1/4... Discriminator Loss: 0.7565... Generator Loss: 1.7593
Epoch 1/4... Discriminator Loss: 0.9663... Generator Loss: 1.0280
Epoch 1/4... Discriminator Loss: 0.5886... Generator Loss: 2.2770

Epoch 1/4... Discriminator Loss: 1.5239... Generator Loss: 0.4799
Epoch 1/4... Discriminator Loss: 0.8669... Generator Loss: 1.6160
Epoch 1/4... Discriminator Loss: 0.8401... Generator Loss: 1.3401
Epoch 1/4... Discriminator Loss: 0.6674... Generator Loss: 2.2473
Epoch 1/4... Discriminator Loss: 0.4897... Generator Loss: 3.5131
Epoch 1/4... Discriminator Loss: 0.7085... Generator Loss: 1.9651
Epoch 1/4... Discriminator Loss: 1.0616... Generator Loss: 0.9423
Epoch 1/4... Discriminator Loss: 0.9837... Generator Loss: 4.5344
Epoch 1/4... Discriminator Loss: 1.2607... Generator Loss: 0.6698
Epoch 1/4... Discriminator Loss: 0.7733... Generator Loss: 1.9289

Epoch 1/4... Discriminator Loss: 1.2790... Generator Loss: 4.1380
Epoch 1/4... Discriminator Loss: 1.3720... Generator Loss: 0.6787
Epoch 1/4... Discriminator Loss: 0.5276... Generator Loss: 2.8020
Epoch 1/4... Discriminator Loss: 0.7115... Generator Loss: 1.8258
Epoch 1/4... Discriminator Loss: 0.7944... Generator Loss: 2.8884
Epoch 1/4... Discriminator Loss: 1.3938... Generator Loss: 0.6767
Epoch 1/4... Discriminator Loss: 0.6595... Generator Loss: 1.9810
Epoch 1/4... Discriminator Loss: 0.6857... Generator Loss: 3.1252
Epoch 1/4... Discriminator Loss: 0.6982... Generator Loss: 1.4637
Epoch 1/4... Discriminator Loss: 1.0516... Generator Loss: 0.9035

Epoch 1/4... Discriminator Loss: 0.8040... Generator Loss: 2.8502
Epoch 1/4... Discriminator Loss: 0.7531... Generator Loss: 1.6232
Epoch 1/4... Discriminator Loss: 0.8975... Generator Loss: 1.3644
Epoch 1/4... Discriminator Loss: 0.7228... Generator Loss: 2.0628
Epoch 1/4... Discriminator Loss: 1.2013... Generator Loss: 0.8539
Epoch 1/4... Discriminator Loss: 0.7933... Generator Loss: 2.1242
Epoch 1/4... Discriminator Loss: 0.7723... Generator Loss: 2.0745
Epoch 1/4... Discriminator Loss: 0.7016... Generator Loss: 1.7057
Epoch 1/4... Discriminator Loss: 0.9369... Generator Loss: 3.6634
Epoch 1/4... Discriminator Loss: 0.9580... Generator Loss: 2.0271

Epoch 1/4... Discriminator Loss: 1.0876... Generator Loss: 0.9100
Epoch 1/4... Discriminator Loss: 1.0450... Generator Loss: 0.9001
Epoch 1/4... Discriminator Loss: 0.8958... Generator Loss: 1.1618
Epoch 1/4... Discriminator Loss: 0.8950... Generator Loss: 2.8749
Epoch 1/4... Discriminator Loss: 1.1152... Generator Loss: 0.7976
Epoch 1/4... Discriminator Loss: 0.9598... Generator Loss: 1.0713
Epoch 1/4... Discriminator Loss: 1.0153... Generator Loss: 0.9033
Epoch 1/4... Discriminator Loss: 0.8127... Generator Loss: 1.1867
Epoch 1/4... Discriminator Loss: 0.9443... Generator Loss: 1.1367
Epoch 1/4... Discriminator Loss: 0.9704... Generator Loss: 1.2862

Epoch 1/4... Discriminator Loss: 1.2820... Generator Loss: 0.6442
Epoch 1/4... Discriminator Loss: 1.0604... Generator Loss: 1.9763
Epoch 1/4... Discriminator Loss: 0.9262... Generator Loss: 1.0451
Epoch 1/4... Discriminator Loss: 0.8784... Generator Loss: 1.0963
Epoch 1/4... Discriminator Loss: 0.7537... Generator Loss: 1.7939
Epoch 1/4... Discriminator Loss: 0.9174... Generator Loss: 1.0345
Epoch 1/4... Discriminator Loss: 0.9091... Generator Loss: 1.1097
Epoch 1/4... Discriminator Loss: 0.9334... Generator Loss: 1.0438
Epoch 1/4... Discriminator Loss: 1.3557... Generator Loss: 0.6795
Epoch 1/4... Discriminator Loss: 0.7631... Generator Loss: 1.7593

Epoch 1/4... Discriminator Loss: 0.8161... Generator Loss: 1.6942
Epoch 1/4... Discriminator Loss: 0.7648... Generator Loss: 1.6943
Epoch 1/4... Discriminator Loss: 0.6372... Generator Loss: 1.7081
Epoch 1/4... Discriminator Loss: 0.9266... Generator Loss: 1.0513
Epoch 1/4... Discriminator Loss: 0.7924... Generator Loss: 2.0322
Epoch 1/4... Discriminator Loss: 1.1254... Generator Loss: 2.7256
Epoch 1/4... Discriminator Loss: 0.8661... Generator Loss: 1.7368
Epoch 1/4... Discriminator Loss: 0.8879... Generator Loss: 1.0951
Epoch 1/4... Discriminator Loss: 1.0729... Generator Loss: 0.8779
Epoch 1/4... Discriminator Loss: 0.8120... Generator Loss: 1.9565

Epoch 1/4... Discriminator Loss: 0.8187... Generator Loss: 1.2910
Epoch 1/4... Discriminator Loss: 0.7099... Generator Loss: 1.8326
Epoch 1/4... Discriminator Loss: 0.8449... Generator Loss: 2.1334
Epoch 1/4... Discriminator Loss: 0.7019... Generator Loss: 1.6730
Epoch 1/4... Discriminator Loss: 0.7714... Generator Loss: 2.3247
Epoch 1/4... Discriminator Loss: 0.7211... Generator Loss: 1.8521
Epoch 1/4... Discriminator Loss: 0.9847... Generator Loss: 1.0925
Epoch 1/4... Discriminator Loss: 0.7645... Generator Loss: 2.0792
Epoch 1/4... Discriminator Loss: 0.8532... Generator Loss: 1.4493
Epoch 1/4... Discriminator Loss: 0.9431... Generator Loss: 1.3055

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.